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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Soiling of building envelope surfaces and its effect on solar reflectance – Part II: Development of an accelerated aging method for roofing materials

Highly reflective roofs can decrease the energy required for building air conditioning, help mitigate the urban heat island effect, and slow global warming. However, these benefits are diminished by soiling and weathering processes that reduce the solar reflectance of most roofing materials. Soiling results from the deposition of atmospheric particulate matter and the growth of microorganisms, each of which absorb sunlight. Weathering of materials occurs with exposure to water, sunlight, and high temperatures. This study developed an accelerated aging method that incorporates features of soiling and weathering. The method sprays a calibrated aqueous soiling mixture of dust minerals, black carbon, humic acid, and salts onto preconditioned coupons of roofing materials, then subjects the soiled coupons to cycles of ultraviolet radiation, heat and water in a commercial weatherometer. Three soiling mixtures were optimized to reproduce the site-specific solar spectral reflectance features of roofing products exposed for 3 years in a hot and humid climate (Miami, Florida); a hot and dry climate (Phoenix, Arizona); and a polluted atmosphere in a temperate climate (Cleveland, Ohio). A fourth mixture was designed to reproduce the three-site average values of solar reflectance and thermal emittance attained after 3 years of natural exposure, which the Cool Roof Rating Council (CRRC) uses to rate roofing products sold in the US. This accelerated aging method was applied to 25 products₋single ply membranes, factory and field applied coatings, tiles, modified bitumen cap sheets, and asphalt shingles₋and reproduced in 3 days the CRRC's 3-year aged values of solar reflectance. In conclusion, this accelerated aging method can be used to speed the evaluation and rating of new cool roofing materials.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

Identifying developmental toxicity pathways for a subset of ToxCast chemicals using human embryonic stem cells and metabolomics

Metabolomics analysis was performed on the supernatant of human embryonic stem (hES) cell cultures exposed to a blinded subset of 11 chemicals selected from the chemical library of EPA's ToxCast Trade-Mark-Sign chemical screening and prioritization research project. Metabolites from hES cultures were evaluated for known and novel signatures that may be indicative of developmental toxicity. Significant fold changes in endogenous metabolites were detected for 83 putatively annotated mass features in response to the subset of ToxCast chemicals. The annotations were mapped to specific human metabolic pathways. This revealed strong effects on pathways for nicotinate and nicotinamide metabolism, pantothenate and CoA biosynthesis, glutathione metabolism, and arginine and proline metabolism pathways. Predictivity for adverse outcomes in mammalian prenatal developmental toxicity studies used ToxRefDB and other sources of information, including Stemina Biomarker Discovery's predictive DevTox Registered-Sign model trained on 23 pharmaceutical agents of known developmental toxicity and differing potency. The model initially predicted developmental toxicity from the blinded ToxCast compounds in concordance with animal data with 73% accuracy. Retraining the model with data from the unblinded test compounds at one concentration level increased the predictive accuracy for the remaining concentrations to 83%. These preliminary results on a 11-chemical subset of the ToxCast chemical library indicate that metabolomics analysis of the hES secretome provides information valuable for predictive modeling and mechanistic understanding of mammalian developmental toxicity. -- Highlights: Black-Right-Pointing-Pointer We tested 11 environmental compounds in a hESC metabolomics platform. Black-Right-Pointing-Pointer Significant changes in secreted small molecule metabolites were observed. Black-Right-Pointing-Pointer Perturbed mass features map to pathways critical for normal development and pregnancy. Black-Right-Pointing-Pointer Arginine, proline, nicotinate, nicotinamide and glutathione pathways were affected.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Encumbering the Copper(I)-Diimine Coordination Sphere with a Benzyl Group: Impact on the Stability and Photoredox Properties

Copper(I)-bisdiimine complexes, [Cu(NN) 2 ] + (where NN represents a diimine such as phenanthroline or bipyridine derivatives), are considered promising photosensitizers for various photochemical applications. However, their effectiveness is subject to several key challenges. In particular, controlling steric strain around the copper(I) center by introducing appropriate substituents (R) at the α-position of the nitrogen atoms is crucial for optimizing the excited-state properties of the complex. In brief, increasing the size of R leads to longer emission lifetimes and higher quantum yields. Additionally, the energy of the singlet excited state rises with increasing steric bulk, enhancing photoinduced reactivity. However, excessive steric strain from bulky substituents can significantly destabilize the coordination sphere. To balance complex stability with increased steric bulk around the metal center, we have developed two novel non-symmetrical ligands featuring branched alkyl chains and benzyl groups at the α-position of the nitrogen atoms. Here, our findings demonstrate that intramolecular π-stacking interactions between the benzyl group and the opposing phenanthroline ligand contribute to stabilizing the coordination sphere. Furthermore, the flexibility of the benzyl group reinforces the tetrahedral geometry around copper(I), resulting in an increased singlet excitedstate energy compared to benchmark complexes. Notably, we show that this enhancement in excited-state energy translates into greater excited-state reactivity.

Copper(I)↗
DOE OSTI2021

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Furthermore, fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

JANA2 Framework for Event Based and Triggerless Data Processing

Development of the second generation JANA2 multi-threaded event processing framework is ongoing through an LDRD initiative grant at Jefferson Lab. The framework is designed to take full advantage of all cores on modern many-core compute nodes. JANA2 efficiently handles both traditional hardware triggered event data and streaming data in online triggerless environments. Development is being done in conjunction with the Electron Ion Collider development. Anticipated to be the next large scale Nuclear Physics facility constructed. The core framework is written in modern C++ but includes an integrated Python interface. The status of development and summary of the more interesting features are presented.

Lawrence, David↗
DOE OSTI2025

Label-based Virtual Directories In dCache

Traditional filesystems organize data in directories. These directories are typically a collection of files whose grouping is based on a single criterion, e.g., the starting date of an experiment, experiment name, beamline ID, measurement device, or instrument. However, each file in a directory can belong to several logical groups, such as a special event type, experiment condition, or a part of a selected dataset. dCache is a storage system developed to store large amounts of scientific data, used by many HEP and Photon Science experiments. With recent developments in dCache, we have introduced a concept of file tagging, which dynamically groups files with the same label into virtual directories. The file labels can be added, removed, renamed, and deleted through the admin interface or via REST API. The files in virtual directories are exposed through all protocols supported by dCache. This contribution will describe the details of the implementation for file tagging in dCache and present our future development plans on automatic metadata extractions, a feature that will significantly simplify data management. Additionally, we are exploring the future use of virtual directories as a way to translate scientific data catalogs into filesystem views for direct data analysis.

Sahakyan, Marina [DESY]↗
DOE OSTI2013

Closely spaced nanomagnets by dual e-beam exposure for low-energy nanomagnet logic

The effect of nanomagnet spacing on required clock field has been studied by micromagnetic simulation for supermalloy (Ni{sub 79}Fe{sub 16}Mo{sub 5}) dots with dimensions 90 Multiplication-Sign 60 Multiplication-Sign 20 nm{sup 3} and 120 Multiplication-Sign 60 Multiplication-Sign 20 nm{sup 3}. Reduction of the inter-magnet spacing for both dimensions has resulted in reduction of the required clock field in the simulation. A dual e-beam exposure technique has been developed to allow fabrication of ultra dense features using conventional poly(methylmethacrylate) e-beam resist. Nanomagnet logic (NML) datalines of supermalloy dots with {approx}10 nm and {approx}15 nm spacing have been fabricated using dual e-beam exposure with a 3{sigma} overlay accuracy of {approx}4 nm. Fabricated NML datalines have been characterized using magnetic force microscopy for various clock fields. Datalines of both spacing have shown proper NML functionality with a clock field as low as 60 mT.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Ultra-narrow ferromagnetic resonance in organic-based thin films grown via low temperature chemical vapor deposition

We present the growth of thin films of the organic-based ferrimagnetic semiconductor V[TCNE]{sub x} (x ∼ 2, TCNE: tetracyanoethylene) via chemical vapor deposition. Under optimized growth conditions, we observe a significant increase in magnetic homogeneity, as evidenced by a Curie temperature above 600 K and sharp magnetization switching. Further, ferromagnetic resonance studies reveal a single resonance with full width at half maximum linewidth of 1.4 G, comparable to the narrowest lines measured in inorganic magnetic materials and in contrast to previous studies that showed multiple resonance features. These characteristics are promising for the development of high frequency electronic devices that take advantage of the unique properties of this organic-based material, such as the potential for low cost synthesis combined with low temperature and conformal deposition on a wide variety of substrates.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Ferroelectric-ferromagnetic multilayers: A magnetoelectric heterostructure with high output charge signal

Multiferroic composites and heterostructures comprising ferroelectric and ferromagnetic materials exhibit room-temperature magnetoelectric (ME) effects greatly exceeding those of single-phase magnetoelectrics known to date. Since these effects are mediated by the interfacial coupling between ferroic constituents, the ME responses may be enhanced by increasing the density of interfaces and improving their quality. A promising material system providing these features is a ferroelectric-ferromagnetic multilayer with epitaxial interfaces. In this paper, we describe theoretically the strain-mediated direct ME effect exhibited by free-standing multilayers composed of single-crystalline ferroelectric nanolayers interleaved by conducting ferromagnetic slabs. Using a nonlinear thermodynamic approach allowing for specific mechanical boundary conditions of the problem, we first calculate the polarization states and dielectric properties of ferroelectric nanolayers in dependence on the lattice mismatch between ferroic constituents and their volume fractions. In these calculations, the ferromagnetic component is described by a model which combines linear elastic behavior with magnetic-field-dependent lattice parameters. Then the quasistatic ME polarization and voltage coefficients are evaluated using the theoretical strain sensitivity of ferroelectric polarization and measured effective piezomagnetic coefficients of ferromagnets. For Pb(Zr₀.₅Ti₀.₅)O₃-FeGaB and BaTiO₃-FeGaB multilayers, the ME coefficients are calculated numerically as a function of the FeGaB volume fraction and used to evaluate the output charge and voltage signals. It is shown that the multilayer geometry of a ferroelectric-ferromagnetic nanocomposite opens the way for a drastic enhancement of the output charge signal. This feature makes biferroic multilayers advantageous for the development of ultrasensitive magnetic-field sensors for technical and biomedical applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Machine learning to analyze images of shocked materials for precise and accurate measurements

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast images of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Collision Tracking in OpenMC: Methods and Applications in Neutron Noise, Neutron Imaging, Time-of-Flight, and Multiplicity Counting

We present the development and application of a collision tracking feature within the OpenMC Monte Carlo particle transport code, designed for diverse applications such as neutron spectroscopy, scatter camera system, neutron noise, and multiplicity counting simulations. This feature enables the tracking of individual particle collisions, with potential applications in nuclear nonproliferation, reactor physics, and nuclear security. Additionally, the feature holds potential for the calibration of neutron detectors, specifically in converting light output into energy deposited within the detectors. The implementation consists of a set of filters—such as reaction type, energy, cell, and material—that constrain the set of collisions that are tracked, extensions to the Python API to enable simple input specification, and support for writing either OpenMC’s native HDF5-based format or the Monte Carlo particle list format. This feature was added to the official OpenMC release in version 0.15.3. In this work, the feature will be applied to showcase scenarios such as time-of-flight simulations, scatter-camera imaging for neutron source localization, neutron-noise analysis to extract integral kinetic parameters such as the prompt decay constant α, and multiplicity counting to estimate the mass of special nuclear materials. Ultimately, this feature aims to expand the application scope of open-source Monte Carlo particle transport codes such as OpenMC.

Monte Carlo code↗
DOE OSTI2017

The machine/job features mechanism

Within the HEPiX virtualization group and the Worldwide LHC Computing Grid’s Machine/Job Features Task Force, a mechanism has been developed which provides access to detailed information about the current host and the current job to the job itself. This allows user payloads to access meta information, independent of the current batch system or virtual machine model. The information can be accessed either locally via the filesystem on a worker node, or remotely via HTTP(S) from a webserver. This paper describes the final version of the specification from 2016 which was published as an HEP Software Foundation technical note, and the design of the implementations of this version for batch and virtual machine platforms. We discuss early experiences with these implementations and how they can be exploited by experiment frameworks.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Ambitious efforts on residual emissions can reduce CO 2 removal and lower peak temperatures in a net-zero future

Carbon dioxide removal (CDR) is expected to play a critical role in reaching net zero CO 2 and especially net zero greenhouse gase (GHG) emissions. However, the extent to which the role of CDR in counterbalancing residual emissions can be reduced has not yet been fully quantified. Here, we use a state-of-the-art integrated assessment model to develop a 'Maximum Sectoral Effort' scenario which features global emissions policies alongside ambitious effort across sectors to reduce their gross GHG emissions and thereby the CDR required for offsets. We find that these efforts can reduce CDR by over 50% globally, increase both the relative and absolute role of the land sink in storing carbon, and more evenly distribute CDR contributions and associated side-effects across regions compared to CO 2 pricing alone. Furthermore, the lower cumulative CO 2 and nonCO 2 emissions leads to earlier and lower peak temperatures. Emphasizing reductions in gross, in addition to net emissions while disallowing the substitution of less durable CDR for offsets can therefore reduce both physical and transition risks associated with high CDR deployment and temperature overshoot.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A Soft-Matter Biomolecular Memristor Synapse for Neuromorphic Systems

The goal of neuromorphic computing is to recreate the computational power and efficiency of the human brain with circuitry. The ability of the brain to solve complex real time tasks, while consuming 20 W of power on average, is made possible through its connection density, adaptability, and parallel processing. Recreating these features using traditional electronics circuit elements is incredibly difficult, and therefore, soft-matter memristors made of biomolecules similar to those found in biological synapses and capable of emulating various synaptic features can be used as neuromorphic hardware. In this work, we introduce and experimentally demonstrate an electronic neuron circuit capable of interacting with ionic, soft-matter memristors. These memristors are proven to exhibit short-term plasticity, especially paired-pulse facilitation and depression found in presynaptic terminals - features that are not found in state-of-the-art solid-state memristors. We make use of these features for applications in online learning by developing a synapse-neuron circuit which implements spike-rate-dependent plasticity (SRDP) as a learning function.

Weiss, Ryan↗
DOE OSTI2012

A Dynamically Reconfigurable System for Closed-Loop Measurements of Network Traffic

Streaming network traffic measurement and analysis is critical for detecting and preventing any real-time anomalies in the network. The high speeds and complexity of today's networks, coupled with ever evolving threats, necessitate closing of the loop between measurements and their analysis in real time. The ensuing system demands high levels of programmability and processing where streaming measurements adapt to the changing network behavior in a goal-oriented manner. In this work, we exploit the features and requirements of the problem and develop an application-specific FPGA-based closed-loop measurement (CLM) system. We make novel use of fine-grained partial dynamic reconfiguration (PDR) as underlying reprogramming paradigm, performing low-latency just-in-time compiled logic changes in FPGA fabric corresponding to the dynamic measurement requirements. Our innovative dynamically reconfigurable socket offers 3× logic savings over conventional static solutions, while offering much reduced reconfiguration latencies over conventional PDR mechanisms. Here, we integrate multiple sockets in a highly parallel CLM framework and demonstrate its effectiveness in identifying heavy flows in streaming network traffic. The results using an FPGA prototype offer 100 percent detection accuracy while sustaining increasing link speeds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Hierarchical OPF Algorithm with Improved Gradient Evaluation in Three-Phase Networks

Linear approximation commonly used in solving alternating-current optimal power flow (AC-OPF) simplifies the system models but incurs accumulated voltage errors in large power networks. Such errors will make the primal-dual type gradient algorithms converge to solutions with voltage violation. In this paper, we improve a recent hierarchical OPF algorithm that rested on primal-dual gradients evaluated with a linearized distribution power flow model. Specifically, we propose a more accurate gradient evaluation method based on an unbalanced three-phase nonlinear distribution power flow model to mitigate the errors arising from linearization. The resultant gradients feature a blocked structure that enables our development of an improved hierarchical primal-dual algorithm to solve the OPF problem. Numerical results on the IEEE 123-bus test feeder and a 4,518-node test feeder show that the proposed method can enhance voltage safety at comparable computational efficiency with the linearized algorithm.

approximation algorithms↗
DOE OSTI2021

InversionNet3D: Efficient and Scalable Learning for 3-D Full-Waveform Inversion

Seismic full-waveform inversion (FWI) techniques aim to find a high-resolution subsurface geophysical model provided with waveform data. Some recent effort in data-driven FWI has shown some encouraging results in obtaining 2-D velocity maps. However, due to high computational complexity and large memory consumption, the reconstruction of 3-D high-resolution velocity maps via deep networks is still a great challenge. Here, in this article, we present InversionNet3D (InvNet3D), an efficient and scalable encoder–decoder network for 3-D FWI. The proposed method employs group convolution in the encoder to establish an effective hierarchy for learning information from multiple sources while cutting down unnecessary parameters and operations at the same time. The introduction of invertible layers further reduces the memory consumption of intermediate features during training and, thus, enables the development of deeper networks with more layers and higher capacity as required by different application scenarios. Experiments on the 3-D Kimberlina dataset demonstrate that InvNet3D achieves state-of-the-art reconstruction performance with lower computational cost and lower memory footprint compared to the baseline.

58 GEOSCIENCES↗
DOE OSTI2011

Phase Stability of t;#8242;-Zirconia-Based Thermal Barrier Coatings: Mechanistic Insights

The temperature capability of yttria-stabilized zirconia thermal barrier coatings (TBCs) is ultimately tied to the rate of evolution of the 'nontransformable' t' phase into a depleted tetragonal form predisposed to the monoclinic transformation on cooling. The t' phase, however, has been shown to decompose in a small fraction of the time necessary to form the monoclinic phase. Instead, a modulated microstructure consisting of a coherent array of Y-rich and Y-lean lamellar phases develops early in the process, with mechanistic features suggestive of spinodal decomposition. Coarsening of this microstructure leads to loss of coherency and ultimately transformation into the monoclinic form, making the kinetics of this process, and not the initial decomposition, the critical factor in determining the phase stability of TBCs. Transmission electron microscopy is shown to be essential not only for characterizing the microstructure but also for proper interpretation of X-ray diffraction analysis.

36 MATERIALS SCIENCE↗